docstr: Simplifying Graph Convolutional Networks
  
  Edge feature is ignored in this model.
  
  Parameters
  ----------
  data_info : dict
      The information about the input dataset.
  embed_size : int
      The dimension of created embedding table. -1 means using original node embedding
  bias : bool
      If True, adds a learnable bias to the output. Default: ``True``.
  k : int
      Number of hops :math:`K`. Defaults:``1``.
docstr_tok: ["Simplifying","Graph","Convolutional","Networks","Edge","feature","is","ignored","in","this","model",".","Parameters","--","--","--","--","--","data_info",":","dict","The","information","about","the","input","dataset",".","embed_size",":","int","The","dimension","of","created","embedding","table",".","-1","means","using","original","node","embedding","bias",":","bool","If","True",",","adds","a","learnable","bias","to","the","output",".","Default",":","``","True","``",".","k",":","int","Number","of","hops",":","math",":","`","K","`",".","Defaults",":","``","1","``","."]
code: def __init__(
      self, data_info: dict, embed_size: int = -1, bias=True, k=2
  ):
      """Simplifying Graph Convolutional Networks
  
      Edge feature is ignored in this model.
  
      Parameters
      ----------
      data_info : dict
          The information about the input dataset.
      embed_size : int
          The dimension of created embedding table. -1 means using original node embedding
      bias : bool
          If True, adds a learnable bias to the output. Default: ``True``.
      k : int
          Number of hops :math:`K`. Defaults:``1``.
      """
      super().__init__()
      self.data_info = data_info
      self.out_size = data_info["out_size"]
      self.embed_size = embed_size
      if embed_size > 0:
          self.embed = nn.Embedding(data_info["num_nodes"], embed_size)
          in_size = embed_size
      else:
          in_size = data_info["in_size"]
      self.sgc = SGConv(
          in_size,
          self.out_size,
          k=k,
          cached=True,
          bias=bias,
          norm=self.normalize,
      )
  
file_path: dgl/dglgo/dglgo/model/node_encoder/sgc.py
imports: import dgl.function
  import torch.nn
  import torch.nn.functional
  from dgl.base import dgl_warning
  from dgl.nn import SGConv
label_desc: Use image node_id 1 to create a new SGC object from inherited base classes: nn with example: obj = SGC(data_info, embed_size, bias, k)